Before you build agentic AI, understand the confused deputy problem Analysis Report

5W1H Analysis

Who

The key stakeholders involved include organisational leaders and developers working within AI and information security sectors, particularly those focused on implementing multi-agent generative AI technologies.

What

The development and implementation of multi-agent generative AI systems are being analysed, focusing on preventing the 'confused deputy problem'—a situation in cyber security where a program mistakenly uses its authority in favour of another request, leading to security vulnerabilities.

When

This analysis stems from continuing developments in AI security protocols discussed in the first half of 2025, highlighting ongoing risks and mitigation strategies necessary for secure AI deployment.

Where

While the issue is globally relevant in the tech industry, the primary focus is likely on markets in technologically advanced regions such as North America, Europe, and parts of Asia where AI technologies are rapidly advancing.

Why

The push for multi-agent systems comes from the increasing demand for more sophisticated AI functions, which require enhanced security to protect vast amounts of data from exploitation, especially as AI systems become more autonomous and complex.

How

Organisations are advised to rethink risk management strategies, ensuring that AI systems are designed with robust security principles that preclude authority exploitation by unauthorised entities.

News Summary

The article focuses on how organisations must strategically manage risks as they embark on projects involving multi-agent generative AI systems. The prioritisation of understanding and mitigating the confused deputy problem is critical to ensuring that these systems do not inadvertently expose vulnerabilities in their operations. As these AI systems become integral to numerous functions, securing them against misuse and exploitation becomes paramount.

6-Month Context Analysis

Over the past six months, there has been an intensified focus on enhancing AI security measures. Industry conferences and publications have increasingly flagged the potential security gaps in emerging AI technologies. The threat scenarios associated with the confused deputy problem have been particularly scrutinised, leading to new research papers and discussions at tech summits. Organisations have been gradually integrating security-focused AI development practices, embracing frameworks that safeguard against authority mismanagement.

Future Trend Analysis

The trend clearly moves towards stronger integration of AI and cybersecurity expertise, leading to the evolution of AI systems that are not only capable but secure. This involves innovative security protocols and collaborative industry standards aimed at addressing vulnerabilities from multilateral agent interactions.

12-Month Outlook

We expect a surge in developing AI software solutions specifically geared towards securing multi-agent systems. Additionally, regulatory bodies may propose new guidelines that reflect the evolving landscape of generative AI security needs.

Key Indicators to Monitor

- Adoption rates of AI security technologies - Regulatory updates on AI security protocols - Incidence reports of confused deputy problem in AI applications - Technological collaborations focusing on multi-agent system security

Scenario Analysis

Best Case Scenario

Organisations successfully integrate advanced security protocols in multi-agent systems, preventing major cyber security breaches and establishing trust in autonomous AI applications across industries.

Most Likely Scenario

A gradual improvement in AI security measures across markets is observable, with some systems achieving high security standards while others continue to fix vulnerabilities through iterative updates and patches.

Worst Case Scenario

Failure to address the confused deputy problem leads to significant security breaches, resulting in data theft and potential damage to AI reliability and market confidence.

Strategic Implications

- Companies must invest in advanced training for AI developers focusing on security. - Collaboration between AI developers and cybersecurity experts is essential. - Development of industry standards for multi-agent system security should be prioritised.

Key Takeaways

  • Integrate robust security strategies before deploying multi-agent AI systems (What)
  • Focus on collaboration between AI and cybersecurity professionals (Who)
  • Monitor advancements and regulatory changes in AI security protocols (Where)
  • Assess your organisation's readiness to tackle the confused deputy problem (How)
  • Prepare to scale solutions across varied geographic and demographic markets (Where)

Source: Before you build agentic AI, understand the confused deputy problem